1. Technical Field
The present invention relates in general to improved grid computing and in particular to efficient client-side estimation of future grid job costs. Still more particularly, the present invention relates to customer estimation of future grid job costs by comparing a current grid job of a particular classification with a history of stored costs for other grid jobs of that customer of that particular classification.
2. Description of the Related Art
Ever since the first connection was made between two computer systems, new ways of transferring data, resources, and other information between two computer systems via a connection continue to develop. In typical network architectures, when two computer systems are exchanging data via a connection, one of the computer systems is considered a client sending requests and the other is considered a server processing the requests and returning results. In an effort to increase the speed at which requests are handled, server systems continue to expand in size and speed. Further, in an effort to handle peak periods when multiple requests are arriving every second, server systems are often joined together as a group and requests are distributed among the grouped servers. Multiple methods of grouping servers have developed such as clustering, multi-system shared data (sysplex) environments, and enterprise systems. With a cluster of servers, one server is typically designated to manage distribution of incoming requests and outgoing responses. The other servers typically operate in parallel to handle the distributed requests from clients. Thus, one of multiple servers in a cluster may service a client request without the client detecting that a cluster of servers is processing the request.
Typically, servers or groups of servers operate on a particular network platform, such as Unix or some variation of Unix, and provide a hosting environment for running applications. Each network platform may provide functions ranging from database integration, clustering services, and security to workload management and problem determination. Each network platform typically offers different implementations, semantic behaviors, and application programming interfaces (APIs).
Merely grouping servers together to expand processing power, however, is a limited method of improving efficiency of response times in a network. Thus, increasingly, within a company network, rather than just grouping servers, servers and groups of server systems are organized as distributed resources. There is an increased effort to collaborate, share data, share cycles, and improve other modes of interaction among servers within a company network and outside the company network. Further, there is an increased effort to outsource nonessential elements from one company network to that of a service provider network. Moreover, there is a movement to coordinate resource sharing between resources that are not subject to the same management system, but still address issues of security, policy, payment, and membership. For example, resources on an individual's desktop are not typically subject to the same management system as resources of a company server cluster. Even different administrative groups within a company network may implement distinct management systems.
The problems with decentralizing the resources available from servers and other computing systems operating on different network platforms, located in different regions, with different security protocols and each controlled by a different management system, has led to the development of Grid technologies using open standards for operating a grid environment. Grid environments support the sharing and coordinated use of diverse resources in dynamic, distributed, virtual organizations. A virtual organization is created within a grid environment when a selection of resources, from geographically distributed systems operated by different organizations with differing policies and management systems, is organized to handle a job request. A grid vendor may develop a grid environment to which a buyer may submit grid jobs, for example.
Grid vendors may offer to process grid jobs with different performance promises and with different pricing policies. Even if standards, such as those proposed by the open standards organization for Grid technologies, define standard monitoring, metering, rating, accounting, and billing interfaces, grid vendors will still have different physical resources available to process grid jobs, and thus pricing and performance will still vary among grid vendors. In one example, grid vendors have to measure the use of the grid vendor's resources by a grid job, which may involve complex formulas which take into account multiple factors, in addition to the actual use of resources. For example, a grid vendor may dedicate a particular processor resource to a particular job and charge the grid job for the dedicated use of the processor, in addition to the actual number of processor cycles the grid job required.
While grid vendors are focused on monitoring, metering, accounting a billing for the actual usage of physical resources, at a computational cycle level, grid clients or customers are focused on processing of applications and jobs at an application type level. As a result, there is a lack of connection between the way that grid customers and grid vendors view the costs associated with grid jobs. Further, currently, each grid vendor still monitors, meters, and bills for grid jobs using different units of physical resource measurement. Thus, because of the disconnect between the client grid job at an application level and the grid vendor measurement of use of physical resources, it is difficult for grid clients to compare the costs of processing grid jobs at different grid vendors and to estimate future costs of submitting grid jobs to a same grid vendor.
Therefore, in view of the foregoing, it would have advantageous to provide a method, system, and program for estimating future job costs by classifying grid jobs in categories with client-defined application based metric units, converting the grid vendor defined metric costs to perform grid jobs into the client-defined application based metric unit costs by category of grid job, and storing the converted client-defined application based metric unit costs for predicting future costs of grid jobs of the same category. In particular, it would be advantageous to submit grid job microcosms, or smaller representative grid jobs, to multiple grid vendors to retrieve actual costs for each category of grid job on a smaller basis, converting the grid vendor defined metric costs to a client-defined application based metric unit cost, and comparing the costs at the client-defined application based metric level, before submitting larger grid jobs, in the future to the most cost effective grid vendor.